21,605 research outputs found
A Study of Actor and Action Semantic Retention in Video Supervoxel Segmentation
Existing methods in the semantic computer vision community seem unable to
deal with the explosion and richness of modern, open-source and social video
content. Although sophisticated methods such as object detection or
bag-of-words models have been well studied, they typically operate on low level
features and ultimately suffer from either scalability issues or a lack of
semantic meaning. On the other hand, video supervoxel segmentation has recently
been established and applied to large scale data processing, which potentially
serves as an intermediate representation to high level video semantic
extraction. The supervoxels are rich decompositions of the video content: they
capture object shape and motion well. However, it is not yet known if the
supervoxel segmentation retains the semantics of the underlying video content.
In this paper, we conduct a systematic study of how well the actor and action
semantics are retained in video supervoxel segmentation. Our study has human
observers watching supervoxel segmentation videos and trying to discriminate
both actor (human or animal) and action (one of eight everyday actions). We
gather and analyze a large set of 640 human perceptions over 96 videos in 3
different supervoxel scales. Furthermore, we conduct machine recognition
experiments on a feature defined on supervoxel segmentation, called supervoxel
shape context, which is inspired by the higher order processes in human
perception. Our ultimate findings suggest that a significant amount of
semantics have been well retained in the video supervoxel segmentation and can
be used for further video analysis.Comment: This article is in review at the International Journal of Semantic
Computin
Who is the director of this movie? Automatic style recognition based on shot features
We show how low-level formal features, such as shot duration, meant as length
of camera takes, and shot scale, i.e. the distance between the camera and the
subject, are distinctive of a director's style in art movies. So far such
features were thought of not having enough varieties to become distinctive of
an author. However our investigation on the full filmographies of six different
authors (Scorsese, Godard, Tarr, Fellini, Antonioni, and Bergman) for a total
number of 120 movies analysed second by second, confirms that these
shot-related features do not appear as random patterns in movies from the same
director. For feature extraction we adopt methods based on both conventional
and deep learning techniques. Our findings suggest that feature sequential
patterns, i.e. how features evolve in time, are at least as important as the
related feature distributions. To the best of our knowledge this is the first
study dealing with automatic attribution of movie authorship, which opens up
interesting lines of cross-disciplinary research on the impact of style on the
aesthetic and emotional effects on the viewers
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks
In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call âtransformational abstractionâ. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to ânuisance variationâ in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
Learning midlevel image features for natural scene and texture classification
This paper deals with coding of natural scenes in order to extract semantic information. We present a new scheme to project natural scenes onto a basis in which each dimension encodes statistically independent information. Basis extraction is performed by independent component analysis (ICA) applied to image patches culled from natural scenes. The study of the resulting coding units (coding filters) extracted from well-chosen categories of images shows that they adapt and respond selectively to discriminant features in natural scenes. Given this basis, we define global and local image signatures relying on the maximal activity of filters on the input image. Locally, the construction of the signature takes into account the spatial distribution of the maximal responses within the image. We propose a criterion to reduce the size of the space of representation for faster computation. The proposed approach is tested in the context of texture classification (111 classes), as well as natural scenes classification (11 categories, 2037 images). Using a common protocol, the other commonly used descriptors have at most 47.7% accuracy on average while our method obtains performances of up to 63.8%. We show that this advantage does not depend on the size of the signature and demonstrate the efficiency of the proposed criterion to select ICA filters and reduce the dimensio
- âŠ